Quality Status Display for a Vibration Welding Process

ABSTRACT

A method includes receiving, during a vibration welding process, a set of sensory signals from a collection of sensors positioned with respect to a work piece during formation of a weld on or within the work piece. The method also includes receiving control signals from a welding controller during the process, with the control signals causing the welding horn to vibrate at a calibrated frequency, and processing the received sensory and control signals using a host machine. Additionally, the method includes displaying a predicted weld quality status on a surface of the work piece using a status projector. The method may include identifying and display a quality status of a suspect weld. The laser projector may project a laser beam directly onto or immediately adjacent to the suspect welds, e.g., as a red, green, blue laser or a gas laser having a switched color filter.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of U.S. patent applicationSer. No. 13/624,233, which was filed on Sep. 21, 2012, and claims thebenefit of U.S. Provisional Application Ser. No. 61/551,665, which wasfiled on Oct. 26, 2011, both of which are hereby incorporated byreference in their entireties.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with U.S. Government support under anAgreement/Project DE-EE0002217, Department of Energy Recovery andReinvestment Act of 2009, Battery Pack Manufacturing B511. The U.S.government may have certain rights in this invention.

TECHNICAL FIELD

The present disclosure relates to the display of weld quality status ina vibration welding process.

BACKGROUND

The process of vibration welding involves the controlled application ofhigh frequency vibration energy to a clamped work piece. Surfacefriction generates heat at a weld interface, which ultimately softensand bonds adjacent work piece surfaces at the interface. The efficiency,consistency, and long term reliability/durability of a vibration weldedassembly depends largely on the welding tools and control methodologyused to form the various welds.

Various closed-loop parameter-based control techniques are typicallyapplied during a vibration welding process to keep certain weldparameters within calibrated limits. This approach can producereasonably consistent welding parameters over time. However, stablewelding process parameters may still fail to produce welds of aconsistently high quality. In addition, external factors such asmaterial quality can also impact weld quality. Moreover, a prematurefailure of just one weld can affect the performance of a welded system.It is therefore common practice in such systems to determine weldintegrity by visual inspection and a laborious process known as picking,wherein each of the formed welds is physically tested by an operatorusing a picking tool.

SUMMARY

A vibration welding monitoring system and method are disclosed hereinthat can be used in conjunction with a vibration welding process. Anexample work piece whose manufacturing process may be enhanced by thepresent approach is a multi-cell battery module having a series ofwelded battery cell tabs. Such a battery module may be configured foruse as a power source, e.g., for an electric traction motor aboard anelectric, hybrid electric, or extended-range electric vehicle. While thepresent approach is not limited to weld process monitoring of batterycell tabs, the battery module described herein is representative of thetype of system in which the present invention may have utility.Therefore, an example battery module is used throughout the remainder ofthis disclosure for illustrative consistency.

In particular, a system is disclosed herein that includes a host machineand a status projector. The host machine is in electrical communicationwith a collection of sensors that generates one or more sensory signalsdescribing various aspects of the welding process, and with a weldingcontroller that generates control signals for controlling the weldinghorn. The host machine processes the sensory and control signals topredict a quality status of welds that are formed using the weldinghorn, possibly including identifying any suspect welds. The host machinethen activates the status projector to display the predicted qualitystatus of the welds on or adjacent to the welds.

A method is also disclosed herein that includes receiving a set ofsensory signals from the sensor(s) during formation of one or more weldson or within a work piece. The method also includes receiving controlsignals from a welding controller of the vibration welding system, withthe control signals causing the welding horn to vibrate at a calibratedfrequency. The host machine predicts the quality status of the variouswelds being formed. The predicted quality status is thereafter displayedon a surface of the work piece using the status projector.

Another system is disclosed that also includes the welding horn, thewelding controller, and the collection of sensors.

The above features and advantages and other features and advantages ofthe present invention are readily apparent from the following detaileddescription of the best modes for carrying out the invention when takenin connection with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of a monitoring system for avibration welding process configured as disclosed herein.

FIG. 2 is a schematic perspective view illustration of a portion of anexample multi-cell battery module whose weld quality may be monitoredand predicted in real-time via the monitoring system shown in FIG. 1.

FIG. 3 is another schematic illustration of the monitoring system shownin FIG. 1.

FIG. 4 is an example diagram of a mapped set of extracted features inmultiple dimensions.

FIG. 5 is a flow chart describing an example method for monitoring andindicating weld quality in a vibration welding process.

DETAILED DESCRIPTION

Referring to the drawings, wherein like reference numbers refer to likecomponents throughout the several Figures, a vibration welding system 11is shown schematically in FIG. 1. The vibration welding system 11includes a welding assembly 12 and a monitoring system 13. Themonitoring system 13 is in communication with the welding assembly 12.Use of the monitoring system 13 is intended to improve productionefficiency by substantially eliminating instances of false acceptsduring a vibration welding process, and by reducing the need for directmanual inspection or picking of every weld in a completed assembly.

The welding assembly 12 shown in FIG. 1 includes a sonotrode/weldinghorn 14 and an anvil 16, along with other welding tools and componentsas described below. The monitoring system 13 is configured to monitorvarious control signals provided by a power supply/welding controller 20and/or measured by a collection of sensors 25 positioned with respect tothe welding apparatus 12. The monitoring system 13 predicts, online andin real time, whether the welding assembly 12 has produced anobjectively passing/good weld or a suspect weld. The suspect welds maythen be subjected to direct end-of-line inspection, such as manualpicking or other testing of the weld, to verify the presence of andisolate any unsatisfactory/bad welds. The verified bad welds may bemarked and imaged as explained below with reference to FIG. 3.

The monitoring system 13 of FIG. 1 may include a status projector 50 andan optional display 59 (see FIG. 3). The status projector 50 interactswith a host machine 40 and a work piece 30 by illuminating any suspectwelds. The status projector 50 may optionally highlight good welds. Suchhighlighting may be performed directly on the weld itself or on anothersurface of the work piece 30, for instance adjacent to the weld, uponformation of the welds. The structure and function of the statusprojector 50 is described in more detail below with reference to FIG. 3.

As will be understood by those of ordinary skill in the art, a weldingcontroller/power supply used for vibration welding, such as the weldingcontroller 20 of FIG. 1, may be electrically-connected to a suitableenergy source, typically a 50-60 Hz wall socket. The welding controller20 may include, for example, voltage rectifiers, transformers, powerinverters, and/or other hardware which ultimately transforms the sourcepower, whatever its form, into vibration control signals (arrow 24). Thecontrol signals (arrow 24) ultimately command predetermined waveformcharacteristic(s), for example a periodic signal having a frequency ofabout 20 kHz to about 40 kHz or more depending on the particular weldingapplication. Other process information may be included in the controlsignals (arrow 24), including but not limited to power traces,displacement of the horn 14, vibration frequency, trigger signals,parameter limit alarms, weld controller fault information, etc., as iswell understood in the art.

Still referring to FIG. 1, the welding system 11 may include a converter22 having mechanical structure sufficient for producing a mechanicalvibration of the horn 14 in response to the control signals (arrow 24).The horn 14 typically includes a knurl pattern 21, typically bumpsand/or ridges, which are suitable for gripping and holding the workpiece 30 when the work piece 30 is clamped between the horn 14 and anvil16. The anvil 16 typically includes a similar knurl pattern 23. Thewelding system 11 of FIG. 1 may also include a booster 18, i.e., anamplifier, which increases the amplitude of any commanded mechanicalvibration from the welding controller 20 as needed.

Within the monitoring system 13 shown in FIG. 1, the host machine 40receives various signals from the sensors 25 positioned with respect tothe welding assembly 12. The specific functionality and structure of thehost machine 40 is described in greater detail below with reference toFIGS. 3 and 5. In general terms, the host machine 40 is a computerdevice in electrical communication with the welding controller 20. Thehost machine 40 is continuously apprised, via receipt of the controlsignals (arrow 24), of instantaneous values of any waveforms transmittedto the horn 14 by the welding controller 20, as well as of other valuesknown by or internal to the welding controller 20. The collection ofsensors 25 is “external” with respect to the internally-generatedcontrol signals (arrow 24) from the welding controller 20, and thereforeis referred to hereinafter as the external sensors 25. The host machine40 shown in FIG. 1 thus receives a set of external signals (arrows 28)from the external sensors 25 and internal control signals (arrow 24)from the welding controller 20. The host machine 40 then processes thesignals (arrows 24, 28) to predict the quality of the weld being formed.

In an example embodiment, the host machine 40 may automatically extracta preliminary set of (n) signal features and map a predetermined numberof the extracted signal features, or alternatively another feature whichis determined as a function of the predetermined number of features, toa dimensional space having demarcated pass/suspect boundaries, a processreferred to hereinafter as a box-void method. The host machine 40 thenpredicts the weld quality of the weld from the map. Other approaches maybe used to predict quality of a weld without departing from the intendedinventive scope, e.g., threshold comparisons, neural network processing,etc. Example extraction and mapping steps for a basic box-void methodare described in detail below with reference to FIGS. 3 and 4.

However it is determined, the predicted weld quality may be provided asan output signal (arrow 26) to the status projector 50, whereupon thepredicted quality of a given weld is visually indicated, e.g., viadirect projection of a beam of light onto the work piece 30. The statusdisplay process is also described below with reference to FIG. 3.

Referring to FIG. 2, a non-limiting example embodiment of the work piece30 of FIG. 1 is a multi-cell battery module 130 having an elongatedconductive interconnecting member 45. For illustrative simplicity, onlya portion of the battery module 130 is shown, however the battery module130 as a whole may include an extended series of interconnecting members45 arranged side by side in one or more rows. Each interconnectingmember 45 joins oppositely-charged battery cell tabs 34, 134, ofadjacent battery cells, with the battery cell tabs 34, 134 formingindividual electrode extensions of a given battery cell.

Each cell tab 34, 134 is internally-welded, below an interconnect board29 of the battery module 130, to the various anodes or cathodescomprising that particular battery cell, as is well understood by thoseof ordinary skill in the art. Multiple battery modules 130 may bearranged to form a complete battery pack of the type used to power anelectric traction motor in a vehicle, or in other relatively high-powerapplications. The interconnecting member 45 may be constructed of asuitable conductive material, e.g., copper and/or nickel, to form aconductive rail for completing an electric circuit. Each interconnectingmember 45 is positioned adjacent to the interconnect board 29 of thebattery module 130.

The battery cell tabs 34, 134 extending from the interconnect board 29may be ultrasonically welded to a longitudinal side wall 49 of a giveninterconnecting member 45, with substantially identical welds 42 formedat each interconnecting member 45 that is used in constructing theoverall battery module 130. The high number of outwardly identical welds42, e.g., on a series of substantially identical interconnecting members45, is a structural feature that may be monitored effectively using thesystem 11.

Referring to FIG. 3, the present monitoring system 13 is described ingreater detail as it may be used for predicting weld quality in a workpiece 230, e.g., a multi-cell battery pack having an extended weldseries 142 of outwardly identical welds. The host machine 40 may includea microprocessor/CPU 47 and a tangible, non-transitory memory device 48possibly including read only memory (ROM), optical, and/or othermagnetic memory. The host machine 40 may also include transitory memory,e.g., any required random access memory (RAM), electrically-programmableread-only memory (EPROM), etc.

The host machine 40 may include additional circuitry such as ahigh-speed clock (not shown), analog-to-digital circuitry,digital-to-analog circuitry, a digital signal processor, and thenecessary input/output devices and other signal conditioning and/orbuffer circuitry. The host machine 40 is configured to execute processinstructions embodying the present method 100, an example of which isshown in FIG. 5, from the memory device 48.

The host machine 40 of FIG. 3 may receive the control signals (arrow 24)from the welding controller 20 and other signals (arrows 28, 128, 228)from the external sensors 25, 125, and 225, respectively. As part of thecontrol signals (arrow 24), the welding controller 20 may providecertain historical information such as welding power over time, weldingfrequency over time, and other possible waveforms or values, as well aspeak values, slopes, areas, area ratios, moving averages, and the like.

In an example embodiment, one external sensor, e.g., the sensors 25, maybe configured as an acoustic sensor, for instance a microphone or anacoustic emission sensor positioned in direct contact with a surface ofa welding tool, e.g., the horn 14 of FIG. 1. The sensors 25 may measurethe acoustic frequency of the vibrating horn 14 of FIG. 1 over time,with this value used by the host machine 40 as a base signal from whichfeatures may be extracted and modeled. Another example external sensor125 may measure a changing linear displacement of the horn 14 of FIG. 1over time. Yet another example external sensor 225 may be used to detectany other desirable value, such as changing welding temperature and/orother atmospheric information such as relative humidity that mightaffect weld quality. Each collection of sensors 25, 125, 225 may includeone or more individual sensors as noted above.

The host machine 40 shown in FIG. 3 may include a recorded featurelibrary 41. The CPU 47 processes the control signals (arrow 24) and theexternal signals (arrows 28, 128, 228) into one or more of the featuresthat are contained in the recorded feature library 41. A preliminary setof signal features may be extracted, i.e., calculated or otherwisederived, for instance as a function of the base power, frequency,displacement, and acoustic signals provided from the welding controller20 and/or from the various sensors 25, 125, or 225. The extractedfeatures may be mapped as set forth below, or some/all of the extractedfeatures may be combined to form another signal feature which is mapped.

Some non-limiting example extracted features include the total weldingenergy, i.e., the area under a power curve or input waveform commandedfrom the welding controller 20. Other extracted features may include theelapsed time in forming a given weld in the weld series 142, peak power,rise time, ramp rate, or even correlation data, for instance between theacoustic signal and the power signal. Any number of desired features maybe extracted and used, either directly as extracted or indirectly bycalculating a derived signal feature as a function of the extractedfeatures, without departing from the intended inventive scope. However,as explained below, the more features that are extracted and used, themore complex the dimensional space needed for mapping these features,thus requiring more processing power.

The host machine 40 may also include a mapping module 43. The mappingmodule 43 includes computer-executable instructions, executable via theCPU 47, for processing the extracted features so as to identify the bestsubset of features that separates a good weld from a suspect weld. Themapping module 43 may also determine suitable upper and/or lower limitsfor any monitored features.

Referring briefly to FIG. 4, an example dimensional space 60 is shown asa simplified illustrative embodiment of an output generated, in thebox-void method noted above, using the mapping module 43 shownschematically in FIG. 3. Various data points 70 are plotted, with eachdata point 70 representing a formed weld. Here, three features (n=3) areused to create an example three-dimensional space. As noted above, nfeatures can be used to produce an n-dimensional space, although then-features may be combined to form a number of signal features that maybe less than n. Therefore, there may be a tradeoff between the number ofextracted features and the resultant complexity of the processing stepsundertaken by the mapping module 43 and CPU 47. Each dimension/axis x,y, and z shown in FIG. 4 corresponds to a separate feature, with thelocation of a given data point 70 described by its location with respectto each of the axes. No other spatial meaning is attributed to theseparticular designators.

A data point 70 corresponding to the feature set of a given weld ismapped to a dimensional space, e.g., the space 60, by the mapping module43. Thus, each data point 70 in the 3D space example of FIG. 4 isdefined by a triplet [x, y, z] in the three-feature example of the sameFigure. Using arbitrary designations for additional axes, a four-axisexample could be defined by a set [x, y, z, q], etc. The mapping module43 of FIG. 3 may access the recorded feature library 41, informationstored in memory device 48, and/or may otherwise determine calibratedlimits for each feature. The upper/lower limits define the boundaries ofthe dimensional space 60, e.g., the cube 71 or a less/more complexgeometric shape in other embodiments mapping fewer/more than threefeatures, respectively. It is also possible that each feature is boundedin only one direction, for instance having a lower limit with no upperlimit or vice versa.

Referring again to FIG. 3, the host machine 40 may also include atraining library 44. One may train the host machine 40 to recognize goodand suspect welds by preloading previously-validated good and suspectdata points 70 (see FIG. 4). These data points 70 can also be used toset the boundaries of the dimensional space used by the mapping module43. The feature library 41 may include all of the data points 70 of FIG.4 in the training library 44, or only some of these points. For example,if the corresponding weld quality of certain data points 70 is not knownbut may be knowable over time given a sufficient number of additionalsimilar samples, the data points 70 of unknown quality may continue toreside in the training library 44 for some time until they arevalidated. Once validated, a new data point 70 in the training library44 may be used to update the boundaries of the dimensional space.

The monitoring system 13 shown in FIG. 3 may also include a qualityprediction module 46. Referring again to FIG. 4, the quality predictionmodule 46 of FIG. 3 may be embodied as a set of computer-executableinstructions, recorded in memory device 48 and executable via the CPU47, for comparing the location of a given data point 70 to the limits orboundaries of the dimensional space, e.g., the cube 71. If a data point70 lies outside of the cube 71, the quality prediction module 46 of FIG.4 may designate these data points 70 as corresponding to a suspect weld.Likewise, if a data point 70 lies within the cube 71, such as in thecase of the data points 72, the quality prediction module 46 maydesignate this data point 70 as corresponding to a good weld.

Referring again to FIG. 3, as will be understood in the art, varioustracking technologies exist which enable identification and tracking ofa component as it moves through various production stages, for instanceRFID tagging, such that the host machine 40 of FIGS. 1 and 3 may beinformed as to the identity of the particular weld that is currentlybeing formed. This allows the good/suspect status for each weld to beaccurately tracked regardless of the number of welds formed before orafter formation of that particular weld.

As noted above, the host machine 40 transmits a quality output signal(arrow 26) which captures the status of a weld. Such a signal may beoutput by the quality prediction module 46 described above. The outputsignal (arrow 26) may be transmitted to a programmable logic controller(PLC) 58, with the good/suspect status viewable in real time byproduction operators via an associated human-machine interface (HMI) 17,or captured and recorded in a database 32. The PLC 58 is in two-waycommunication with the host machine 40, e.g., via a data bus (notshown). The HMI 17 may be a touch-screen display so as to facilitatedirect user interaction with the host machine 40, the status projector50, the display 59, and/or a camera 80 that executes image processinginstructions 101, and that is in communication with the host machine 40over a bus 36, or otherwise in communication with database 32.

The database 32 of FIG. 3 is shown as a single device for simplicity.However, the database 32 may be embodied as a distributed databasemanagement system. For instance, the database 32 may be embodied as adata server storing a calibrated number of signal files from theexternal sensors 25, 125, 225 and/or the welding controller 20, a dataacquisition/DAQ database, a structured query library/SQL databasecontaining metadata and quality data for a calibrated time period, etc.Any data in the database 32 may be extracted by the host machine 40, asis indicated by double-headed arrow 39.

The host machine 40 of FIG. 3 may also transmit the output signal 26status to the status projector 50 and PLC 58. As described above,certain types of work pieces, such as an assembled battery module 230shown schematically in FIG. 3, include a lengthy series of outwardlyidentical welds. These are collectively illustrated as a weld series142. Each weld in the weld series 142 is typically manually inspected ina laborious picking process after weld formation, wherein the variouswelds are physically pulled or prodded with a picking tool to stress theweld and thus directly verify weld quality. Use of the status projector50 may help minimize the amount of time spent and ergonomic stresses ofmanually picking welds in the battery module 230, and may facilitate orexpedite the minimal picking of suspect welds that still occurs.

Specifically, the status projector 50 includes a processor 55. Theprojector 50 displays status information using one or more light beams(arrows 52) by projecting the light beams (arrows 52) onto a surface,for instance on or adjacent to the work piece 230 on or adjacent tosuspect welds in the weld series 142. The status projector 50 may beembodied as a conventional light projector, or as a laser projectorwhich projects concentrated or collimated beams of visible or otherwavelengths of light as explained below.

The processor 55 receives the output signal (arrow 26) from the PLC 58and/or from the host machine 40. The output signal (arrow 26) mayinclude the associated identifying information such as the serial numberof the battery module 230 and identifying information for each weld inthe weld series 142. The processor 55 then projects a lightbeam(s)(arrows 52) onto or adjacent to a weld. Optionally, display 59may be placed in communication with the PLC 58 and positioned withrespect to the work piece 230, with text or other information (arrow 62)from the PLC 58 presented via the display 59, such as the weld status,serial number of the work piece 230, alert messages, status information,etc.

For instance, a light beam (arrows 52) may be projected onto a portionof the example interconnecting member 45 of FIG. 2, or onto the weld 42that is deemed to be suspect. The status projector 50, when configuredas an optional laser projector, may use a red/green/blue (RGB) laserprojector to project a specific color laser indicating the suspectwelds, or a gas laser with a switched color filter. The color of thebeam should provide sufficient contrast with the materials onto whichthe light beam 52 is directed, with optional mixing of the beams (arrows52) enabling use of colors such as yellow, magenta, cyan, etc.

Using the light beams (arrows 52) in this manner, line operators may bevisually queued to the suspect welds. Other embodiments may beconceived, such as coating work piece surfaces, such as theinterconnecting member 45 of FIG. 2, with a fluorescent layer and usingultraviolet light rather than visible light as the light beams (arrows52). Visible light is then emitted from the irradiated surfaces similarto approaches used in certain heads up display (HUD) systems.

An example method 100 is shown in FIG. 5. The method 100 may be embodiedas computer-executable instructions that are executed by the hostmachine 40 of FIG. 3 and other components of the monitoring system 13 tomonitor weld quality in a welded assembly, e.g., the example batterymodule 130 of FIG. 2.

Beginning with step 102, the work piece 30 of FIG. 1 is clamped intoposition between the horn 14 and the anvil 16, and calibrated vibrationsare applied to the clamped work piece 30.

As step 102 is executed, the power supply 20 and the external sensors 25measure certain parameters and environmental data at step 104, with thisinformation being relayed to and recorded by the host machine 40.

At step 106, the host machine 40 processes the received data, i.e., thecontrol signals (arrow 24) and the external signals (arrows 28) of FIG.1, and predicts the quality of the weld being formed. Step 106 mayentail execution of the box-void method noted above, or any otherpredictive approach, including possibly threshold parameterscomparisons, neural network processing, and the like. Once the predictedquality of a given weld is recorded, the method 100 proceeds to step108.

At step 108, the quality status is displayed on or adjacent to the weldsof the work piece using the status projector 50, e.g., by projectinglight beams (arrows 52) directly onto the welds from overhead or ontopart of the interconnecting member 45 shown in FIG. 2. The light beams(arrows 52) visually highlight or indicate the suspect welds 42 directlyon or adjacent to the welds 42. The method 100 then proceeds to step110.

At step 110, an inspector may manually pick the welds 42 that areindicated as being suspect at step 108. The inspector may then recordsthe locations of the welds 42 that are in fact unsatisfactory/bad,either as part of step 110 or by proceeding to optional step 112.

At optional step 112, as shown in phantom, the inspector may physicallymark the confirmed bad welds from step 110. Step 112 may entailphysically placing stickers, imprints, paint, or any other suitablemarker. The marker may be placed over or next to a confirmedunsatisfactory weld. As accurate identification of the weld locations isessential, the markers used in step 112 should be designed in such a waythat the position of the placed marker can be readily and accuratelydetermined via operation of the camera 80 and the image processinginstructions 101, even under varying lighting conditions. Likewise, thework piece, e.g., a battery section, should be located consistently inthe field of view (arrow 53) of the camera 80 to ensure that thelocations of the welds are determined accurately. Alternatively,additional visual locating features can be added to the battery sectionto make locating of the part more accurate for the image processinginstructions 101. The method 100 then proceeds to optional step 114.

At optional step 114, the camera 80 may be used to image any marked badwelds 42 by executing the instructions 101. The captured images of theconfirmed bad welds are processed by executing the instructions 101.Processing may include identifying the locations in or on the work pieceof each confirmed bad weld, e.g., by comparing the location of theimaged markers to a baseline/calibrated image or using other position orpattern recognition techniques. The locations of the unsatisfactorywelds may be recorded in the database 32 for use by a repair technicianin a subsequent repair operation.

The repair technician may be restricted to updating of the repair statusonly of the unsatisfactory welds. For instance, the HMI 17 of the PLC 58or another HMI may display the unsatisfactory weld locations and/orimages of these welds instead of displaying all of the welds andallowing the technician to pick from a list of all welds. Given thenumber of welds in the weld series 142, restriction of data entry toonly confirmed unsatisfactory welds may reduce errors, such as byselecting the wrong weld location from a global list. If other positionsrequire data entry, a warning message may be given to the repair personrequesting manual confirmation of the position.

Communication may be made with the PLC 58 to indentify when a particularweld 42 has been identified for repair. Any such image displayed on theHMI 17 or other display should be taken such that buttons on the touchscreen of HMI 17 properly with the location of weld positions in theimage. This alignment can help ensure that image processing software ofprojector 50 or another device can confirm that the inspector/repairperson is selecting the correct weld position when entering data on badwelds. Information on bad welds may be fed automatically via the HMI 17to the host machine 40 of FIG. 1 to improve estimation/predictionresults over time.

While the best modes for carrying out the invention have been describedin detail, those familiar with the art to which this invention relateswill recognize various alternative designs and embodiments forpracticing the invention within the scope of the appended claims.

1. A method comprising: receiving, during a vibration welding process, aset of sensory signals from a collection of sensors positioned withrespect to a work piece during formation of a weld on or within the workpiece; receiving control signals from a welding controller during thevibration welding process, wherein the control signals cause the weldinghorn to vibrate at a calibrated frequency; processing the receivedsensory and control signals using a host machine; predicting, via thehost machine, a quality status of the weld; and displaying the predictedquality status on a surface of the work piece using a status projector.2. The method of claim 1, wherein predicting the quality status of theweld includes identifying at least one suspect weld, and whereindisplaying the predicted quality status includes displaying the qualitystatus of the at least one suspect weld.
 3. The method of claim 1,further comprising displaying the quality status of only the at leastone suspect weld.
 4. The method of claim 1, wherein predicting thequality status includes mapping a predetermined number of signalfeatures of at least some of the signals to a dimensional space having anumber of dimensions that is proportional to the predetermined number.5. The method of claim 1, wherein displaying the quality status on asurface of the work piece includes projecting visible light onto oradjacent to the weld using a laser projector.
 6. The method of claim 1,wherein displaying the quality status on a surface of the work pieceincludes projecting non-visible light onto or adjacent to the weld. 7The method of claim 1, further comprising: identifying at least onesuspect weld; positioning a marker on each weld of the at least onesuspect weld; and recording an image of each positioned marker using acamera.
 8. The method of claim 7, further comprising: automaticallydetermining the position of each positioned marker within the work pieceusing a processor; and recording the position in memory of the hostmachine.
 9. The method of claim 1, wherein the work piece is a batterymodule having a conductive interconnecting member that is connected, viaa plurality of the welds, to a set of battery tabs, and whereindisplaying the quality status includes projecting visible light directlyonto or immediately adjacent to the conductive interconnecting member.